1. Introduction: Big Data and AI in a Changing M&E Landscape

The fourth industrial revolution (4IR) and advances in information technology have created vast quantities of digital data generated in real time and in different formats.

According to Bernard Okpe’s article, Big Data Analytics and Artificial Intelligence are contributing to changes in Monitoring and Evaluation by increasing efficiency, reducing evaluation costs and creating new possibilities for development monitoring and assessment.

These technologies are also supporting:

• Deeper citizen participation in decision-making.
• Greater access to data and information.
• Improved transparency and accountability.
• Predictive modelling and large-scale forecasting.
• More effective monitoring of development operations.

2. Three Key Messages

Source: Okpe, eVALUation Matters, Second Quarter 2020, page 35.

3. Understanding Big Data Analytics and Artificial Intelligence

Big Data Analytics

The article describes Big Data in terms of the large volume of digital data generated daily through digital services, technologies and tools, together with methods used to analyse large datasets.

It highlights how information generated through different sources can be analysed to support decision-making and provide insights into development interventions.

Artificial Intelligence

The article describes AI as a branch of computer science that facilitates the development of machines capable of performing tasks requiring human intelligence.

It also explains that AI systems analyse data and adjust to new inputs, allowing technologies to be designed for specific interventions.

The Relationship Between Big Data and AI

According to the article, Big Data and AI reinforce each other. AI depends on the quality of the data integrated into Big Data systems, while AI enables the analysis of large datasets to derive insights and support predictions.

Source: Okpe, pages 35–36.

4.1 Transport Sector: Ridesharing Applications

The article discusses the use of data generated through taxi-hailing applications such as Uber, Taxify and Little Cab in Kenya’s transport sector.

These applications generate information about people’s travel patterns and the use of transport services.

How the Technology Works

According to the article, the system collects and analyses several types of information:

• Location data from mobile phones.
• Information about frequently used roads.
• Travel patterns at different times of the day.
• Geolocation information from vehicles.
• News reports, blogs and social media posts concerning traffic conditions.

These sources contribute to a database that can be used to identify road conditions and traffic congestion patterns.

Implications for Monitoring and Evaluation

The article identifies several implications of this approach:

1. Travellers can access information that helps drivers select alternative routes during periods of congestion.
2. Government authorities can obtain information about the most congested roads.
3. Decision-makers can use this information when considering road network improvements.
4. Big Data can complement conventional evaluation methodologies by providing timely, longitudinal and easily analysable information.
5. Information about road conditions can support decisions concerning road infrastructure development.

Source: Okpe, pages 37–38.

4.2 Healthcare Sector: AI-Powered Digital Healthcare Assistant

The article discusses the application of AI in Kenya’s healthcare sector through Dr. Elsa, described as a data-powered AI healthcare assistant intended to improve affordable and high-quality access to healthcare.

At the time covered by the article, its application was described as being in the early stages in Kenya.

Applications Described in the Article

• Supporting clinical decision-making.
• Assisting with the diagnosis of patients.
• Supporting electronic health records.
• Providing information to assist healthcare practitioners in clinical decisions.
• Supporting patient self-diagnosis based on symptoms.

Data Sources

The article explains that AI systems in healthcare can use information from several sources, including:

• Electronic health records.
• Information about diseases and patient symptoms.
• Prescribed medications.
• Treatment outcomes.
• Laboratory testing and diagnostic information.

Combining and analysing these different sources can help identify relationships between symptoms, treatments and outcomes.

Implications for Monitoring and Evaluation

The article highlights the potential of Big Data and AI to support:

1. Monitoring and evaluation of therapeutic techniques.
2. Assessment of the effectiveness of medicines and treatment procedures.
3. Identification of anomalies in diagnosis.
4. Mapping areas that may be prone to particular diseases.
5. Faster and more targeted public health responses.

Source: Okpe, page 38.

4.3 Agricultural Sector: Drones and Agricultural Monitoring

The article describes the use of drones in Kenya’s agricultural sector and their potential contribution to agricultural production, monitoring and evaluation.

Drones can provide information about:

• Soil quality.
• The presence of pests.
• Nutrient deficiencies.
• The extent of farm coverage.
• The condition of crops and soils across large areas.

Data Sources and Collection

The article identifies several sources of agricultural information used in connection with drone technology:

• Smartphone applications.
• Social media posts from farmers regarding farm performance.
• News articles.
• Government agency data on soil nutrient levels.
• Agricultural market reports.

These data are used to create descriptive maps that are fed into drone GPS systems. Geolocation information helps identify areas with agricultural defects.

Implications for Monitoring and Evaluation

The article identifies several potential contributions:

1. Monitoring agricultural conditions across large areas.
2. Assessing the condition of crops and soils.
3. Identifying pest infestations and weather-related issues.
4. Supporting appropriate corrective measures.
5. Reducing the time and resources required for large-scale agricultural assessment.
6. Providing information that can support agricultural evaluation.

Challenges Identified

The article also identifies limitations associated with drone technology:

• Not every evaluator can afford the technology.
• Not every evaluator has the necessary capacity to operate drones.

The article suggests that adequate planning and training of M&E professionals can help address these challenges.

Source: Okpe, pages 38–39.

5. Comparing the Three Sector Applications

The following table summarises the applications described in the original article.

6. Implications for Monitoring and Evaluation

Across the three sectors, the article demonstrates how digital technologies can complement conventional M&E approaches.

6.1 Complementing Traditional Data Sources

Big Data can provide up-to-date information and additional perspectives that complement traditional data sources, helping to create a more comprehensive outlook of a situation.

6.2 Improving Data Availability and Analysis

The article identifies the potential of Big Data to provide information that is timely, longitudinal, relatively inexpensive to obtain and easily analysable.

6.3 Supporting Decision-Making

Information generated through AI and Big Data can help decision-makers understand conditions in different sectors and inform decisions about interventions and resource allocation.

6.4 Improving Monitoring Capabilities

Digital sensors, mobile devices, online information and drones create additional opportunities to collect information and monitor changes over time and across geographical areas.

7. Opportunities and Risks of Big Data and AI in M&E

The article stresses that sectors using these technologies need to ensure individual data are protected. It also calls for transparent consideration of risks and clear communication of safeguards concerning confidentiality and data governance.

Source: Okpe, page 40.

8. Conclusion and Lessons for M&E Professionals

The article concludes that countries across Africa require investment in critical sectors such as education, technology, healthcare, agriculture and transport. Better investment decisions require quality data.

It argues that M&E is evolving and becoming increasingly equipped with technologies that can support development interventions and results.

Its recommendations and observations highlight the importance of:

1. Utilising Big Data and AI opportunities in M&E.
2. Developing appropriate infrastructure to leverage these technologies.
3. Protecting personal information and addressing ethical data collection concerns.
4. Ensuring confidentiality and appropriate data governance.
5. Maintaining data quality standards to improve reliability and consistency.
6. Sensitising policymakers on the use of Big Data and AI results in policy development.

9. Knowledge Assessment

Test your understanding of the article. The following questions are developed by EvalCommunity Academy using information presented in the original publication.

10. Reflection Questions for M&E Professionals

Consider the following questions in relation to the applications described in the article:

1. How can Big Data complement traditional monitoring and evaluation data sources?
2. What opportunities do mobile applications and geolocation information create for transport monitoring?
3. How can AI-supported healthcare information contribute to monitoring treatment outcomes?
4. What types of agricultural information can drones provide to M&E professionals?
5. What infrastructure and technical capacity are required to integrate these technologies into M&E systems?
6. How can organisations protect personal information when collecting and analysing Big Data?
7. Why is data quality important when using AI and Big Data for development decisions?

12. Original Article and References